# coding=utf-8 # Copyright 2023 The Salesforce Team Authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TensorFlow BLIP model.""" from __future__ import annotations import warnings from dataclasses import dataclass from typing import Any, Optional, Tuple, Union import tensorflow as tf from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling from ...modeling_tf_utils import ( TFPreTrainedModel, get_initializer, get_tf_activation, keras, keras_serializable, shape_list, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds, stable_softmax from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_blip import BlipConfig, BlipTextConfig, BlipVisionConfig from .modeling_tf_blip_text import BLIP_TEXT_INPUTS_DOCSTRING, TFBlipTextLMHeadModel, TFBlipTextModel logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "Salesforce/blip-vqa-base" # Copied from transformers.models.clip.modeling_tf_clip.contrastive_loss def contrastive_loss(logits: tf.Tensor) -> tf.Tensor: return tf.math.reduce_mean( keras.metrics.sparse_categorical_crossentropy( y_true=tf.range(shape_list(logits)[0]), y_pred=logits, from_logits=True ) ) # Copied from transformers.models.clip.modeling_tf_clip.clip_loss with clip->blip def blip_loss(similarity: tf.Tensor) -> tf.Tensor: caption_loss = contrastive_loss(similarity) image_loss = contrastive_loss(tf.transpose(similarity)) return (caption_loss + image_loss) / 2.0 @dataclass class TFBlipForConditionalGenerationModelOutput(ModelOutput): """ Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. This class also adds the loss term from the text decoder. Args: loss (`tf.Tensor`, *optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`): Languge modeling loss from the text decoder. logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`, *optional*): Prediction scores of the language modeling head of the text decoder model. image_embeds (`tf.Tensor` of shape `(batch_size, output_dim)`, *optional*): The image embeddings obtained after applying the Vision Transformer model to the input image. last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.` """ loss: Tuple[tf.Tensor] | None = None logits: Tuple[tf.Tensor] | None = None image_embeds: tf.Tensor | None = None last_hidden_state: Optional[tf.Tensor] = None hidden_states: Tuple[tf.Tensor, ...] | None = None attentions: Tuple[tf.Tensor, ...] | None = None @property def decoder_logits(self): warnings.warn( "`decoder_logits` attribute is deprecated and will be removed in version 5 of Transformers." " Please use the `logits` attribute to retrieve the final output instead.", FutureWarning, ) return self.logits @dataclass class TFBlipTextVisionModelOutput(ModelOutput): """ Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. This class also adds the loss term from the text decoder. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Languge modeling loss from the text decoder. image_embeds (`tf.Tensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): The image embeddings obtained by applying the projection layer to the pooler_output. last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None image_embeds: tf.Tensor | None = None last_hidden_state: Optional[tf.Tensor] = None hidden_states: Tuple[tf.Tensor, ...] | None = None attentions: Tuple[tf.Tensor, ...] | None = None @dataclass class TFBlipImageTextMatchingModelOutput(ModelOutput): """ Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. This class also adds the loss term from the text decoder as well as the image-text similarity scores. Args: itm_score (`tf.Tensor`): The image-text similarity scores. loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Languge modeling loss from the text decoder. image_embeds (`tf.Tensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): The image embeddings obtained by applying the projection layer to the pooler_output. last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. vision_pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`, *optional*): Last layer hidden-state of the vision of the vision-only branch of the model. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. question_embeds (`tf.Tensor`): The question embeddings obtained by the text projection layer. """ itm_score: tf.Tensor | None = None loss: tf.Tensor | None = None image_embeds: tf.Tensor | None = None last_hidden_state: Optional[tf.Tensor] = None hidden_states: Tuple[tf.Tensor, ...] | None = None vision_pooler_output: tf.Tensor | None = None attentions: Tuple[tf.Tensor, ...] | None = None question_embeds: Tuple[tf.Tensor] | None = None @dataclass class TFBlipOutput(ModelOutput): """ Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. logits_per_image:(`tf.Tensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text:(`tf.Tensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds(`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`BlipTextModel`]. image_embeds(`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`BlipVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`BlipTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): The output of the [`BlipVisionModel`]. """ loss: tf.Tensor | None = None logits_per_image: Optional[tf.Tensor] = None logits_per_text: Optional[tf.Tensor] = None text_embeds: Optional[tf.Tensor] = None image_embeds: Optional[tf.Tensor] = None text_model_output: TFBaseModelOutputWithPooling = None vision_model_output: TFBaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) class TFBlipVisionEmbeddings(keras.layers.Layer): def __init__(self, config: BlipVisionConfig, **kwargs): super().__init__(**kwargs) self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.patch_embedding = keras.layers.Conv2D( filters=self.embed_dim, kernel_size=self.patch_size, strides=self.patch_size, kernel_initializer=get_initializer(self.config.initializer_range), data_format="channels_last", name="patch_embedding", ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 def build(self, input_shape=None): self.class_embedding = self.add_weight( shape=(1, 1, self.embed_dim), initializer=get_initializer(self.config.initializer_range), trainable=True, name="class_embedding", ) self.position_embedding = self.add_weight( shape=(1, self.num_positions, self.embed_dim), initializer=get_initializer(self.config.initializer_range), trainable=True, name="position_embedding", ) if self.built: return self.built = True if getattr(self, "patch_embedding", None) is not None: with tf.name_scope(self.patch_embedding.name): self.patch_embedding.build([None, None, None, 3]) def call(self, pixel_values: tf.Tensor) -> tf.Tensor: # Input is channels-first, we transpose. PyTorch transposes after the conv because PyTorch # likes channels-first convs. batch_size = tf.shape(pixel_values)[0] pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) patch_embeds = self.patch_embedding(pixel_values) patch_embeds = tf.reshape(patch_embeds, (batch_size, self.num_patches, -1)) class_embeds = tf.broadcast_to(self.class_embedding, (batch_size, 1, self.embed_dim)) embeddings = tf.concat([class_embeds, patch_embeds], axis=1) embeddings = embeddings + self.position_embedding[:, : tf.shape(embeddings)[1], :] return embeddings # Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextEmbeddings with CLIP->Blip class TFBlipTextEmbeddings(keras.layers.Layer): def __init__(self, config: BlipTextConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.hidden_size self.config = config def build(self, input_shape: tf.TensorShape = None): with tf.name_scope("token_embedding"): self.weight = self.add_weight( shape=(self.config.vocab_size, self.embed_dim), initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range), trainable=True, name="weight", ) with tf.name_scope("position_embedding"): self.position_embedding = self.add_weight( shape=(self.config.max_position_embeddings, self.embed_dim), initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range), trainable=True, name="embeddings", ) super().build(input_shape) def call( self, input_ids: Optional[tf.Tensor] = None, position_ids: Optional[tf.Tensor] = None, inputs_embeds: Optional[tf.Tensor] = None, ) -> tf.Tensor: """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ if input_ids is None and inputs_embeds is None: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: check_embeddings_within_bounds(input_ids, self.config.vocab_size) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if position_ids is None: position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0) position_embeds = tf.gather(params=self.position_embedding, indices=position_ids) position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1)) final_embeddings = inputs_embeds + position_embeds return final_embeddings class TFBlipAttention(keras.layers.Layer): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = keras.layers.Dropout(config.attention_dropout, name="dropout") self.qkv = keras.layers.Dense( 3 * self.embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="qkv" ) self.projection = keras.layers.Dense( self.embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="projection" ) def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = False, training: Optional[bool] = None, ) -> Tuple[tf.Tensor, tf.Tensor | None, Tuple[tf.Tensor] | None]: """Input shape: Batch x Time x Channel""" bsz, tgt_len, embed_dim = shape_list(hidden_states) mixed_qkv = self.qkv(hidden_states) mixed_qkv = tf.reshape(mixed_qkv, (bsz, tgt_len, 3, self.num_heads, self.head_dim)) mixed_qkv = tf.transpose(mixed_qkv, perm=(2, 0, 3, 1, 4)) query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2] # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = query_states @ tf.transpose(key_states, (0, 1, 3, 2)) attention_scores = attention_scores * self.scale # Normalize the attention scores to probabilities. attention_probs = stable_softmax(attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = tf.transpose(attention_probs @ value_states, perm=(0, 2, 1, 3)) new_context_layer_shape = shape_list(context_layer)[:-2] + [self.embed_dim] context_layer = tf.reshape(context_layer, new_context_layer_shape) output = self.projection(context_layer) outputs = (output, attention_probs) if output_attentions else (output, None) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dropout", None) is not None: with tf.name_scope(self.dropout.name): self.dropout.build(None) if getattr(self, "qkv", None) is not None: with tf.name_scope(self.qkv.name): self.qkv.build([None, None, self.embed_dim]) if getattr(self, "projection", None) is not None: with tf.name_scope(self.projection.name): self.projection.build([None, None, self.embed_dim]) class TFBlipMLP(keras.layers.Layer): def __init__(self, config: BlipConfig, **kwargs): super().__init__(**kwargs) self.activation_fn = get_tf_activation(config.hidden_act) in_proj_std = (config.hidden_size**-0.5) * ((2 * config.num_hidden_layers) ** -0.5) fc_std = (2 * config.hidden_size) ** -0.5 self.fc1 = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(fc_std), name="fc1" ) self.fc2 = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(in_proj_std), name="fc2" ) self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.fc1(inputs=hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(inputs=hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "fc1", None) is not None: with tf.name_scope(self.fc1.name): self.fc1.build([None, None, self.config.hidden_size]) if getattr(self, "fc2", None) is not None: with tf.name_scope(self.fc2.name): self.fc2.build([None, None, self.config.intermediate_size]) class TFBlipEncoderLayer(keras.layers.Layer): def __init__(self, config: BlipConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.hidden_size self.self_attn = TFBlipAttention(config, name="self_attn") self.layer_norm1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1") self.mlp = TFBlipMLP(config, name="mlp") self.layer_norm2 = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, output_attentions: Optional[bool] = False, training: Optional[bool] = None, ) -> Tuple[tf.Tensor]: """ Args: hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`tf.Tensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, head_mask=attention_mask, output_attentions=output_attentions, training=training, ) hidden_states = hidden_states + residual residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = hidden_states + residual outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self_attn", None) is not None: with tf.name_scope(self.self_attn.name): self.self_attn.build(None) if getattr(self, "layer_norm1", None) is not None: with tf.name_scope(self.layer_norm1.name): self.layer_norm1.build([None, None, self.embed_dim]) if getattr(self, "mlp", None) is not None: with tf.name_scope(self.mlp.name): self.mlp.build(None) if getattr(self, "layer_norm2", None) is not None: with tf.name_scope(self.layer_norm2.name): self.layer_norm2.build([None, None, self.embed_dim]) class TFBlipPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = BlipConfig base_model_prefix = "blip" _keys_to_ignore_on_load_missing = [r"position_ids"] BLIP_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. Parameters: config ([`BlipConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ BLIP_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`BlipImageProcessor`]. See [`BlipImageProcessor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ BLIP_INPUTS_DOCSTRING = r""" Args: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoProcessor`]. See [`BlipProcessor.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`BlipImageProcessor`]. See [`BlipImageProcessor.__call__`] for details. return_loss (`bool`, *optional*): Whether or not to return the contrastive loss. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @keras_serializable class TFBlipEncoder(keras.layers.Layer): config_class = BlipConfig """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`BlipEncoderLayer`]. Args: config (`BlipConfig`): The corresponding vision configuration for the `BlipEncoder`. """ def __init__(self, config: BlipConfig, **kwargs): super().__init__(**kwargs) self.config = config self.layers = [TFBlipEncoderLayer(config, name=f"layers_._{i}") for i in range(config.num_hidden_layers)] @unpack_inputs def call( self, inputs_embeds, attention_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = None, ) -> Union[Tuple, TFBaseModelOutput]: r""" Args: inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Embedded representation of the inputs. Should be float, not int tokens. attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) layer_outputs = encoder_layer( hidden_states, attention_mask, output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layers", None) is not None: for layer in self.layers: with tf.name_scope(layer.name): layer.build(None) class TFBlipVisionModel(TFBlipPreTrainedModel): main_input_name = "pixel_values" config_class = BlipVisionConfig def __init__(self, config: BlipVisionConfig, *args, **kwargs): super().__init__(config, *args, **kwargs) self.config = config self.embeddings = TFBlipVisionEmbeddings(config, name="embeddings") self.encoder = TFBlipEncoder(config, name="encoder") self.post_layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="post_layernorm") self.embed_dim = config.hidden_size def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling: hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFBaseModelOutputWithPooling( last_hidden_state=output.last_hidden_state, pooler_output=output.pooler_output, hidden_states=hs, attentions=attns, ) @unpack_inputs @add_start_docstrings_to_model_forward(BLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=BlipVisionConfig) def call( self, pixel_values: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = None, ) -> Union[Tuple, TFBaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.embeddings(pixel_values) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.post_layernorm(last_hidden_state) pooled_output = last_hidden_state[:, 0, :] # TF gets confused if we call the layer with inputs of different ranks, so insert a singleton dimension pooled_output = self.post_layernorm(tf.expand_dims(pooled_output, 1)) pooled_output = tf.squeeze(pooled_output, 1) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return TFBaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def get_input_embeddings(self): return self.embeddings def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "post_layernorm", None) is not None: with tf.name_scope(self.post_layernorm.name): self.post_layernorm.build([None, None, self.embed_dim]) class TFBlipMainLayer(keras.layers.Layer): config_class = BlipConfig def __init__(self, config: BlipConfig, *args, **kwargs): super().__init__(*args, **kwargs) if not isinstance(config.text_config, BlipTextConfig): raise TypeError( "config.text_config is expected to be of type BlipTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.vision_config, BlipVisionConfig): raise TypeError( "config.vision_config is expected to be of type BlipVisionConfig but is of type" f" {type(config.vision_config)}." ) text_config = config.text_config vision_config = config.vision_config self.projection_dim = config.projection_dim self.text_embed_dim = text_config.hidden_size self.vision_embed_dim = vision_config.hidden_size self.text_model = TFBlipTextModel(text_config, name="text_model") self.vision_model = TFBlipVisionModel(vision_config, name="vision_model") self.visual_projection = keras.layers.Dense( self.projection_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="visual_projection", ) self.text_projection = keras.layers.Dense( self.projection_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="text_projection", ) self.config = config def build(self, input_shape=None): self.logit_scale = self.add_weight( name="logit_scale", shape=[], initializer=keras.initializers.Constant(self.config.logit_scale_init_value), trainable=True, ) if self.built: return self.built = True if getattr(self, "text_model", None) is not None: with tf.name_scope(self.text_model.name): self.text_model.build(None) if getattr(self, "vision_model", None) is not None: with tf.name_scope(self.vision_model.name): self.vision_model.build(None) if getattr(self, "visual_projection", None) is not None: with tf.name_scope(self.visual_projection.name): self.visual_projection.build([None, None, self.vision_embed_dim]) if getattr(self, "text_projection", None) is not None: with tf.name_scope(self.text_projection.name): self.text_projection.build([None, None, self.text_embed_dim]) @unpack_inputs def call( self, input_ids: tf.Tensor | None = None, pixel_values: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = None, ) -> Union[Tuple, TFBlipOutput]: # Use BLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / tf.norm(image_embeds, ord=2, axis=-1, keepdims=True) text_embeds = text_embeds / tf.norm(text_embeds, ord=2, axis=-1, keepdims=True) # cosine similarity as logits logit_scale = tf.exp(self.logit_scale) logits_per_text = tf.matmul(text_embeds, image_embeds, transpose_b=True) * logit_scale logits_per_image = tf.transpose(logits_per_text) loss = None if return_loss: loss = blip_loss(logits_per_text) loss = tf.reshape(loss, (1,)) if not return_dict: output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return ((loss,) + output) if loss is not None else output return TFBlipOutput( loss=loss, logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) class TFBlipModel(TFBlipPreTrainedModel): config_class = BlipConfig _keys_to_ignore_on_load_missing = [r"text_decoder.cls.predictions.decoder.bias"] main_input_name = "input_ids" def __init__(self, config: BlipConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.blip = TFBlipMainLayer(config, name="blip") def serving_output(self, output: TFBlipOutput) -> TFBlipOutput: return TFBlipOutput( logits_per_image=output.logits_per_image, logits_per_text=output.logits_per_text, text_embeds=output.text_embeds, image_embeds=output.image_embeds, ) @unpack_inputs @add_start_docstrings_to_model_forward(BLIP_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBlipOutput, config_class=BlipConfig) def call( self, input_ids: tf.Tensor | None = None, pixel_values: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = None, ) -> Union[Tuple, TFBlipOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, TFBlipModel >>> model = TFBlipModel.from_pretrained("Salesforce/blip-image-captioning-base") >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True ... ) >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = tf.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities ```""" outputs = self.blip( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, position_ids=position_ids, return_loss=return_loss, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs @add_start_docstrings_to_model_forward(BLIP_TEXT_INPUTS_DOCSTRING) def get_text_features( self, input_ids: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, return_dict: Optional[bool] = None, ) -> tf.Tensor: r""" Returns: text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`TFBlipTextModel`]. Examples: ```python >>> from transformers import AutoProcessor, TFBlipModel >>> model = TFBlipModel.from_pretrained("Salesforce/blip-image-captioning-base") >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf") >>> text_features = model.get_text_features(**inputs) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict text_outputs = self.blip.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, return_dict=return_dict, ) pooled_output = text_outputs[1] text_features = self.blip.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(BLIP_VISION_INPUTS_DOCSTRING) def get_image_features( self, pixel_values: tf.Tensor | None = None, return_dict: Optional[bool] = None, ) -> tf.Tensor: r""" Returns: image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`TFBlipVisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, TFBlipModel >>> model = TFBlipModel.from_pretrained("Salesforce/blip-image-captioning-base") >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="tf") >>> image_features = model.get_image_features(**inputs) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.blip.vision_model(pixel_values=pixel_values, return_dict=return_dict) pooled_output = vision_outputs[1] # pooled_output image_features = self.blip.visual_projection(pooled_output) return image_features def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "blip", None) is not None: with tf.name_scope(self.blip.name): self.blip.build(None) @add_start_docstrings( """ BLIP Model for image captioning. The model consists of a vision encoder and a text decoder. One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the text decoder continue the prompt. Otherwise, the decoder starts generating text from the [BOS] (beginning-of-sequence) token. will start generating the caption from the text input. If no text input is provided, the decoder will start with the [BOS] token only. """, BLIP_START_DOCSTRING, ) class TFBlipForConditionalGeneration(TFBlipPreTrainedModel): config_class = BlipConfig _keys_to_ignore_on_load_missing = [r"text_decoder.cls.predictions.decoder.bias"] main_input_name = "pixel_values" def __init__(self, config: BlipConfig, *args, **kwargs): super().__init__(config, *args, **kwargs) self.vision_model = TFBlipVisionModel(config.vision_config, name="vision_model") self.text_decoder = TFBlipTextLMHeadModel(config.text_config, name="text_decoder") self.decoder_input_ids = config.text_config.bos_token_id self.decoder_pad_token_id = config.text_config.pad_token_id def get_input_embeddings(self) -> keras.layers.Layer: return self.vision_model.embeddings.patch_embedding @unpack_inputs @add_start_docstrings_to_model_forward(BLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBlipForConditionalGenerationModelOutput, config_class=BlipConfig) def call( self, pixel_values: tf.Tensor, input_ids: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, labels: tf.Tensor | None = None, return_dict: Optional[bool] = None, training: Optional[bool] = None, ) -> Union[Tuple, TFBlipForConditionalGenerationModelOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, TFBlipForConditionalGeneration >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") >>> model = TFBlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> text = "A picture of" >>> inputs = processor(images=image, text=text, return_tensors="tf") >>> outputs = model(**inputs) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) image_embeds = vision_outputs[0] outputs = self.text_decoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=image_embeds, labels=labels, return_dict=False, training=training, ) if not return_dict: outputs = (outputs[0], outputs[1], image_embeds, vision_outputs[0]) + vision_outputs[2:] return tuple(output for output in outputs if output is not None) if labels is not None: loss = outputs[0] logits = outputs[1] else: loss = None logits = outputs[0] if loss is not None and loss.shape.rank == 0: loss = tf.reshape(loss, (1,)) return TFBlipForConditionalGenerationModelOutput( loss=loss, logits=logits, image_embeds=image_embeds, last_hidden_state=vision_outputs.last_hidden_state, hidden_states=vision_outputs.hidden_states, attentions=vision_outputs.attentions, ) def generate( self, pixel_values: tf.Tensor, input_ids: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, **generate_kwargs, ) -> tf.Tensor: r""" Overrides *generate* function to be able to use the model as a conditional generator Parameters: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, image_height, image_width)`: Input image to be processed input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): The sequence used as a prompt for the generation. attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, TFBlipForConditionalGeneration >>> model = TFBlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="tf") >>> outputs = model.generate(**inputs) >>> print(processor.decode(outputs[0], skip_special_tokens=True)) two cats sleeping on a couch ``` """ batch_size = pixel_values.shape[0] vision_outputs = self.vision_model(pixel_values=pixel_values) image_embeds = vision_outputs[0] image_attention_mask = tf.ones(shape_list(image_embeds)[:-1], dtype=tf.int32) if isinstance(input_ids, list): input_ids = tf.convert_to_tensor(input_ids, dtype=tf.int32) elif input_ids is None: input_ids = tf.convert_to_tensor( [[self.decoder_input_ids, self.config.text_config.eos_token_id]], dtype=tf.int32 ) input_ids = tf.tile(input_ids, (batch_size, 1)) # PyTorch: input_ids[:, 0] = self.config.text_config.bos_token_id input_ids = tf.concat( [tf.ones((batch_size, 1), dtype=tf.int32) * self.config.text_config.bos_token_id, input_ids[:, 1:]], axis=1 ) attention_mask = attention_mask[:, :-1] if attention_mask is not None else None outputs = self.text_decoder.generate( input_ids=input_ids[:, :-1], eos_token_id=self.config.text_config.sep_token_id, pad_token_id=self.config.text_config.pad_token_id, attention_mask=attention_mask, encoder_hidden_states=image_embeds, encoder_attention_mask=image_attention_mask, **generate_kwargs, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "vision_model", None) is not None: with tf.name_scope(self.vision_model.name): self.vision_model.build(None) if getattr(self, "text_decoder", None) is not None: with tf.name_scope(self.text_decoder.name): self.text_decoder.build(None) @add_start_docstrings( """ BLIP Model for visual question answering. The model consists of a vision encoder, a text encoder as well as a text decoder. The vision encoder will encode the input image, the text encoder will encode the input question together with the encoding of the image, and the text decoder will output the answer to the question. """, BLIP_START_DOCSTRING, ) class TFBlipForQuestionAnswering(TFBlipPreTrainedModel): config_class = BlipConfig _keys_to_ignore_on_load_missing = [r"text_decoder.cls.predictions.decoder.bias"] def __init__(self, config: BlipConfig, *args, **kwargs): super().__init__(config, *args, **kwargs) self.vision_model = TFBlipVisionModel(config.vision_config, name="vision_model") self.text_encoder = TFBlipTextModel(config.text_config, name="text_encoder", add_pooling_layer=False) self.text_decoder = TFBlipTextLMHeadModel(config.text_config, name="text_decoder") self.decoder_pad_token_id = config.text_config.pad_token_id self.decoder_start_token_id = config.text_config.bos_token_id def get_input_embeddings(self) -> keras.layers.Layer: return self.vision_model.embeddings.patch_embedding # Adapted from transformers.models.t5.modeling_tf_t5.TFT5PreTrainedModel._shift_right def _shift_right(self, input_ids): decoder_start_token_id = self.decoder_start_token_id pad_token_id = self.decoder_pad_token_id if decoder_start_token_id is None or pad_token_id is None: raise ValueError("decoder_start_token_id and pad_token_id must be defined!") start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id) start_tokens = tf.cast(start_tokens, input_ids.dtype) # Ensure compatible dtypes for concatenation shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = tf.where( shifted_input_ids == -100, tf.cast(tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids.dtype), shifted_input_ids, ) # "Verify that `labels` has only positive values and -100" tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=shifted_input_ids.dtype)) return shifted_input_ids @unpack_inputs @add_start_docstrings_to_model_forward(BLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBlipTextVisionModelOutput, config_class=BlipVisionConfig) def call( self, input_ids: tf.Tensor, pixel_values: tf.Tensor | None = None, decoder_input_ids: tf.Tensor | None = None, decoder_attention_mask: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, labels: tf.Tensor | None = None, return_dict: Optional[bool] = None, training: Optional[bool] = None, ) -> Union[Tuple, TFBlipTextVisionModelOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, TFBlipForQuestionAnswering >>> model = TFBlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> # training >>> text = "How many cats are in the picture?" >>> label = "2" >>> inputs = processor(images=image, text=text, return_tensors="tf") >>> labels = processor(text=label, return_tensors="tf").input_ids >>> inputs["labels"] = labels >>> outputs = model(**inputs) >>> loss = outputs.loss >>> # inference >>> text = "How many cats are in the picture?" >>> inputs = processor(images=image, text=text, return_tensors="tf") >>> outputs = model.generate(**inputs) >>> print(processor.decode(outputs[0], skip_special_tokens=True)) 2 ```""" if labels is None and decoder_input_ids is None: raise ValueError( "Either `decoder_input_ids` or `labels` should be passed when calling" " `TFBlipForQuestionAnswering`. if you are training the model make sure that `labels` is passed, if you" " are using the model for inference make sure that `decoder_input_ids` is passed or call `generate`" ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) image_embeds = vision_outputs[0] image_attention_mask = tf.ones(shape_list(image_embeds)[:-1], dtype=tf.int64) question_embeds = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=image_embeds, encoder_attention_mask=image_attention_mask, return_dict=return_dict, training=training, ) question_embeds = question_embeds[0] if not return_dict else question_embeds.last_hidden_state if labels is not None and decoder_input_ids is None: # labels are already shifted right, see: https://github.com/huggingface/transformers/pull/23153 decoder_input_ids = labels answer_output = self.text_decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=question_embeds, encoder_attention_mask=attention_mask, labels=labels, return_dict=return_dict, training=training, ) if labels is not None: decoder_loss = tf.reduce_mean(answer_output.loss) if return_dict else tf.reduce_mean(answer_output[0]) else: decoder_loss = None if not return_dict: outputs = (decoder_loss, image_embeds, vision_outputs[0]) + vision_outputs[2:] return tuple(output for output in outputs if output is not None) return TFBlipTextVisionModelOutput( loss=decoder_loss, image_embeds=image_embeds, last_hidden_state=vision_outputs.last_hidden_state, hidden_states=vision_outputs.hidden_states, attentions=vision_outputs.attentions, ) def generate( self, input_ids: tf.Tensor, pixel_values: tf.Tensor, attention_mask: tf.Tensor | None = None, **generate_kwargs, ) -> tf.Tensor: r""" Overrides *generate* function to be able to use the model as a conditional generator Parameters: input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): The sequence used as a prompt for the generation. pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, image_height, image_width)`: Input image to be processed attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`. `1` for tokens that are NOT MASKED, `0` for MASKED tokens. generate_kwargs (dict, *optional*): Additional arguments passed to the `generate` function of the decoder Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, TFBlipForQuestionAnswering >>> model = TFBlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base") >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> text = "How many cats are in the picture?" >>> inputs = processor(images=image, text=text, return_tensors="tf") >>> outputs = model.generate(**inputs) >>> print(processor.decode(outputs[0], skip_special_tokens=True)) 2 ``` """ vision_outputs = self.vision_model(pixel_values=pixel_values) image_embeds = vision_outputs[0] image_attention_mask = tf.ones(shape_list(image_embeds)[:-1], dtype=tf.int32) if isinstance(input_ids, list): input_ids = tf.Tensor(input_ids) question_outputs = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=image_embeds, encoder_attention_mask=image_attention_mask, return_dict=False, ) question_embeds = question_outputs[0] question_attention_mask = tf.ones(shape_list(question_embeds)[:-1], dtype=tf.int32) bos_ids = tf.fill( (tf.shape(question_embeds)[0], 1), value=tf.cast(self.decoder_start_token_id, input_ids.dtype) ) outputs = self.text_decoder.generate( input_ids=bos_ids, eos_token_id=self.config.text_config.sep_token_id, pad_token_id=self.config.text_config.pad_token_id, encoder_hidden_states=question_embeds, encoder_attention_mask=question_attention_mask, **generate_kwargs, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "vision_model", None) is not None: with tf.name_scope(self.vision_model.name): self.vision_model.build(None) if getattr(self, "text_encoder", None) is not None: with tf.name_scope(self.text_encoder.name): self.text_encoder.build(None) if getattr(self, "text_decoder", None) is not None: with tf.name_scope(self.text_decoder.name): self.text_decoder.build(None) @add_start_docstrings( """ BLIP Model with a vision and text projector, and a classification head on top. The model is used in the context of image-text retrieval. Given an image and a text, the model returns the probability of the text being relevant to the image. """, BLIP_START_DOCSTRING, ) class TFBlipForImageTextRetrieval(TFBlipPreTrainedModel): config_class = BlipConfig def __init__(self, config: BlipConfig, *args, **kwargs): super().__init__(config, *args, **kwargs) self.vision_model = TFBlipVisionModel(config.vision_config, name="vision_model") self.text_encoder = TFBlipTextModel(config.text_config, name="text_encoder", add_pooling_layer=False) # vision projection layer self.vision_proj = keras.layers.Dense( config.image_text_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="vision_proj", ) # text projection layer self.text_proj = keras.layers.Dense( config.image_text_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="text_proj", ) # image text matching head self.itm_head = keras.layers.Dense( 2, kernel_initializer=get_initializer(config.initializer_range), name="itm_head" ) self.decoder_pad_token_id = ( config.text_config.pad_token_id if not hasattr(config, "decoder_pad_token_id") else config.decoder_pad_token_id ) self.decoder_start_token_id = ( config.text_config.bos_token_id if not hasattr(config, "decoder_start_token_id") else config.decoder_start_token_id ) self.config = config def get_input_embeddings(self) -> keras.layers.Layer: return self.vision_model.embeddings.patch_embedding @unpack_inputs @add_start_docstrings_to_model_forward(BLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBlipImageTextMatchingModelOutput, config_class=BlipVisionConfig) def call( self, input_ids: tf.Tensor, pixel_values: tf.Tensor | None = None, use_itm_head: Optional[bool] = True, attention_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = None, ) -> Union[Tuple, TFBlipImageTextMatchingModelOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, TFBlipForImageTextRetrieval >>> model = TFBlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco") >>> processor = AutoProcessor.from_pretrained("Salesforce/blip-itm-base-coco") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> text = "an image of a cat" >>> inputs = processor(images=image, text=text, return_tensors="tf") >>> outputs = model(**inputs) ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) image_embeds = vision_outputs[0] image_atts = tf.ones(shape_list(image_embeds)[:-1], dtype=tf.int64) # Matt: In PyTorch, only one path (itm/non-itm) is taken. However, in TensorFlow this can result in # some layers not being built! To avoid this, we always call both paths, then use an if statement to select # which output to pass to the final output. The unnecessary nodes will be pruned from the final graph, but # not before the layers have all been built correctly. itm_question_embeds = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=image_embeds, encoder_attention_mask=image_atts, return_dict=return_dict, training=training, ) itm_question_embeds = itm_question_embeds[0] if not return_dict else itm_question_embeds.last_hidden_state itm_output = self.itm_head(itm_question_embeds[:, 0, :]) no_itm_question_embeds = self.text_encoder( input_ids=input_ids, attention_mask=attention_mask, return_dict=return_dict, training=training, ) no_itm_question_embeds = ( no_itm_question_embeds[0] if not return_dict else no_itm_question_embeds.last_hidden_state ) image_feat, _ = tf.linalg.normalize(self.vision_proj(image_embeds[:, 0, :]), ord=2, axis=-1) text_feat, _ = tf.linalg.normalize(self.text_proj(no_itm_question_embeds[:, 0, :]), ord=2, axis=-1) no_itm_output = tf.matmul(image_feat, text_feat, transpose_b=True) if use_itm_head: output = itm_output question_embeds = itm_question_embeds else: output = no_itm_output question_embeds = no_itm_question_embeds if not return_dict: outputs = (output, vision_outputs[0]) + vision_outputs[2:] + (question_embeds,) return tuple(output for output in outputs if output is not None) return TFBlipImageTextMatchingModelOutput( itm_score=output, last_hidden_state=vision_outputs.last_hidden_state, hidden_states=vision_outputs.hidden_states, attentions=vision_outputs.attentions, question_embeds=question_embeds, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "vision_model", None) is not None: with tf.name_scope(self.vision_model.name): self.vision_model.build(None) if getattr(self, "text_encoder", None) is not None: with tf.name_scope(self.text_encoder.name): self.text_encoder.build(None) if getattr(self, "vision_proj", None) is not None: with tf.name_scope(self.vision_proj.name): self.vision_proj.build([None, None, self.config.vision_config.hidden_size]) if getattr(self, "text_proj", None) is not None: with tf.name_scope(self.text_proj.name): self.text_proj.build([None, None, self.config.text_config.hidden_size]) if getattr(self, "itm_head", None) is not None: with tf.name_scope(self.itm_head.name): self.itm_head.build([None, None, self.config.text_config.hidden_size]) __all__ = [ "TFBlipModel", "TFBlipPreTrainedModel", "TFBlipForConditionalGeneration", "TFBlipForQuestionAnswering", "TFBlipVisionModel", "TFBlipTextModel", "TFBlipForImageTextRetrieval", ]